Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models

• URL: http://arxiv.org/abs/2409.09306v1
• Date: Sat, 14 Sep 2024 05:07:57 GMT
• Title: Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models
• Authors: Dewen Zhang, Wangpeng An, Hayaru Shouno,
• Abstract summary: We introduce a new method for generating such data by integrating human keypoints with traditional visual features like captions and bounding boxes. Our approach produces datasets designed for fine-tuning models to excel in human-centric activities. Experimental results show an overall improvement of 21.18% compared to the original LLaVA-7B model.
• Score: 1.9890559505377343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current multimodal models are well-suited for general visual understanding tasks. However, they perform inadequately when handling complex visual tasks related to human poses and actions, primarily due to the lack of specialized instruction-following data. We introduce a new method for generating such data by integrating human keypoints with traditional visual features like captions and bounding boxes. Our approach produces datasets designed for fine-tuning models to excel in human-centric activities, focusing on three specific types: conversation, detailed description, and complex reasoning. We fine-tuned the LLaVA-7B model with this novel dataset, achieving significant improvements across various human pose-related tasks. Experimental results show an overall improvement of 21.18% compared to the original LLaVA-7B model. These findings demonstrate the effectiveness of keypoints-assisted data in enhancing multimodal models.

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